Distributed Bayesian target tracking with reduced communication: Likelihood consensus 2.0
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Date
2024-02-01
Authors
Šauša, Erik
Rajmic, Pavel
Hlawatsch, Franz
ORCID
0000-0002-8381-4442Advisor
Referee
Mark
Journal Title
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Publisher
Elsevier
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Abstract
The likelihood consensus (LC) enables Bayesian target tracking in a decentralized sensor network with possibly nonlinear and non-Gaussian sensor characteristics. Here, we propose an evolved LC methodology—dubbed “LC 2.0”—with significantly reduced intersensor communication. LC 2.0 uses multiple refinements of the original LC including a sparsity-promoting calculation of expansion coefficients, the use of a B-spline dictionary, a distributed adaptive calculation of the relevant state-space region, and efficient binary representations. We consider the use of the proposed LC 2.0 within a distributed particle filter and within a distributed particle-based probabilistic data association filter. Our simulation results demonstrate that a reduction of intersensor communication by a factor of about 190 can be obtained without compromising the tracking performance.
Description
Citation
SIGNAL PROCESSING. 2024, vol. 215, issue February 2024, p. 1-13.
https://www.sciencedirect.com/science/article/pii/S016516842300333X
https://www.sciencedirect.com/science/article/pii/S016516842300333X
Document type
Peer-reviewed
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Published version
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Language of document
en
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Defence
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Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
http://creativecommons.org/licenses/by-nc-nd/4.0/